class_docstr: Stepwise learning rate schedule.
class_docstr_tok: ["Stepwise","learning","rate","schedule","."]
code: def __call__(self, step):
      linear_warmup = self.lr_warmup_init + (
          tf.cast(step, dtype=tf.float32)
          / self.lr_warmup_step
          * (self.adjusted_lr - self.lr_warmup_init)
      )
      learning_rate = tf.where(
          step < self.lr_warmup_step, linear_warmup, self.adjusted_lr
      )
      lr_schedule = [
          [1.0, self.lr_warmup_step],
          [0.1, self.first_lr_drop_step],
          [0.01, self.second_lr_drop_step],
      ]
      for mult, start_global_step in lr_schedule:
          learning_rate = tf.where(
              step < start_global_step,
              learning_rate,
              self.adjusted_lr * mult,
          )
      return learning_rate
  
file_path: DALI/docs/examples/use_cases/tensorflow/efficientdet/model/utils/optimizers.py
imports: from absl import logging
  import numpy
  import tensorflow
label_desc: Use image node_id 2 for calling the StepwiseLrSchedule obj's underlying member method code with example usage: obj.__call__(step) and returns: learning_rate